This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The MEME/MAST project allows biomedical scientists to model molecular sequences and identify relationships and patterns among them. This aids in understanding the structure and function of genes and proteins in the cell. MEME is a pattern discov[unreadable]ery tool, based on statistical learning algorithms, that finds sequence patterns in groups of proteins or genes. These patterns are useful for understanding the important biological features of the sequences. Furthermore, they can be used by the MAST algorithm to identify genes and proteins that share the patterns found by MEME, and are therefore likely to be func[unreadable]tionally or evolutionarily related to the original group of sequences. MEME and MAST comprise an extremely powerful method for identifying distant but important relationships among biological molecules.